# Multivariate Wireless Link Quality Prediction Based on Pre-trained Large Language Models

arXiv: https://arxiv.org/abs/2501.11247 · HTML: https://mlsanity.com/paper/2501.11247/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 20 Jan 2025.

## Builds on (most cited first, 8 of 8)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [One Fits All:Power General Time Series Analysis by Pretrained LM](https://mlsanity.com/paper/2302.11939/) (2023)
- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [Large Language Model (LLM)-enabled Graphs in Dynamic Networking](https://mlsanity.com/paper/2407.20840/) (2024)
- [MultiCast: Zero-Shot Multivariate Time Series Forecasting Using LLMs](https://mlsanity.com/paper/2405.14748/) (2024)
- [Using Pre-trained LLMs for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2501.06386/) (2025)
- [Point Cloud-based Proactive Link Quality Prediction for Millimeter-wave Communications](https://mlsanity.com/paper/2301.00752/) (2023)

## Abstract (arXiv)

Accurate and reliable link quality prediction (LQP) is crucial for optimizing network performance, ensuring communication stability, and enhancing user experience in wireless communications. However, LQP faces significant challenges due to the dynamic and lossy nature of wireless links, which are influenced by interference, multipath effects, fading, and blockage. In this paper, we propose GAT-LLM, a novel multivariate wireless link quality prediction model that combines Large Language Models (LLMs) with Graph Attention Networks (GAT) to enable accurate and reliable multivariate LQP of wireless communications. By framing LQP as a time series prediction task and appropriately preprocessing the input data, we leverage LLMs to improve the accuracy of link quality prediction. To address the limitations of LLMs in multivariate prediction due to typically handling one-dimensional data, we integrate GAT to model interdependencies among multiple variables across different protocol layers, enhancing the model's ability to handle complex dependencies. Experimental results demonstrate that GAT-LLM significantly improves the accuracy and robustness of link quality prediction, particularly in multi-step prediction scenarios.